Association between mental health symptoms and behavioral performance in younger vs. older online workers

Colleen Mills-Finnerty, Halee Staggs, Nichole Hogoboom, Sharon Naparstek, Tiffany Harvey, Sherry A. Beaudreau, Ruth O’Hara

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: The COVID-19 pandemic has been associated with increased rates of mental health problems, particularly in younger people. Objective: We quantified mental health of online workers before and during the COVID-19 pandemic, and cognition during the early stages of the pandemic in 2020. A pre-registered data analysis plan was completed, testing the following three hypotheses: reward-related behaviors will remain intact as age increases; cognitive performance will decline with age; mood symptoms will worsen during the pandemic compared to before. We also conducted exploratory analyses including Bayesian computational modeling of latent cognitive parameters. Methods: Self-report depression (Patient Health Questionnaire 8) and anxiety (General Anxiety Disorder 7) prevalence were compared from two samples of Amazon Mechanical Turk (MTurk) workers ages 18–76: pre-COVID 2018 (N = 799) and peri-COVID 2020 (N = 233). The peri-COVID sample also completed a browser-based neurocognitive test battery. Results: We found support for two out of three pre-registered hypotheses. Notably our hypothesis that mental health symptoms would increase in the peri-COVID sample compared to pre-COVID sample was not supported: both groups reported high mental health burden, especially younger online workers. Higher mental health symptoms were associated with negative impacts on cognitive performance (speed/accuracy tradeoffs) in the peri-COVID sample. We found support for two hypotheses: reaction time slows down with age in two of three attention tasks tested, whereas reward function and accuracy appear to be preserved with age. Conclusion: This study identified high mental health burden, particularly in younger online workers, and associated negative impacts on cognitive function.

Original languageEnglish
Article number995445
JournalFrontiers in Psychiatry
Volume14
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
Copyright © 2023 Mills-Finnerty, Staggs, Hogoboom, Naparstek, Harvey, Beaudreau and O’Hara.

Keywords

  • Bayesian analysis
  • COVID-19
  • anxiety
  • behavior and cognition
  • computational modeling
  • depression

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